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Circle Representation for Medical Object Detection
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DOI：10.1109/TMI.2021.3122835.png)
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摘要
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Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet.
Keyword:
Object detection
Biomedical imaging
Head
Feature extraction
Heating systems
Convolutional neural networks
Training
Anchor-free
CircleNet
detection
image analysis
pathology
期刊
IF:
9.8
论文数:
6.3K
被引数:
3.7W
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